Adaptive resonance theory (ART) architectures are neural networks that self-organize stable recognition categories in real time in response to arbitrary sequences of input patterns. The basic principles of adaptive resonance theory were introduced in Grossberg, "Adaptive Pattern Classification and Universal Recoding, II: Feedback, Expectation, Olfaction and Illusions," Biological Cybernetics, 23 (1976) 187-202. Three classes of adaptive resonance architectures have since been characterized as systems of differential equations by Gail A. Carpenter and Stephen Grossberg.
The first class, ART 1, self-organizes recognition categories for arbitrary sequences of binary input patterns. See Carpenter and Grossberg, "Category Learning and Adaptive Pattern Recognition: A Neural Network Model," Proceedings of the 3rd Army Conference on Applied Mathematics and Computing, ARO Report 86-1 (1985) 37-56, and "A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine," Computer Vision, Graphics, and Image Processing, 37 (1987) 54-115. One implementation of an ART 1 system is presented in U.S. Application Ser. No. PCT/US86/02553, filed Nov. 26, 1986 by Carpenter and Grossberg for "Pattern Recognition System".
A second class, ART2, accomplishes the same as ART 1 but for either binary or analog inputs. See Carpenter and Grossberg, "ART2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns," Applied Optics, 26 (1987) 4919-4930. One implementation of an ART2 system is presented in U.S. Pat. No. 4,914,708 issued Apr. 3, 1990 to Carpenter and Grossberg for "System for Self-Organization of Stable Category Recognition Codes for Analog Input Patterns".
A third class, ART3, is based on ART2 but includes a model of the chemical synapse that solves the memory search problem of ART systems employed in network hierarchies in which learning can be either fast or slow and category representations can be distributed or compressed. See Carpenter and Grossberg, "ART3: Hierarchical Search Using Chemical Transmitters in Self-Organizing Pattern Recognition Architectures," Neural Networks, 3 (1990) 129-152. Also see U.S. patent application Ser. No. 07/464,247 filed Jan. 12, 1990.